Variable step size stagewise adaptive matching pursuit algorithm for image compressed sensing

Xue, Bin; Chen, Xiangdong; Yang, Jian

doi:10.1109/icspcc.2013.6663917

Cited by 13 publications

(7 citation statements)

References 20 publications

(22 reference statements)

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“…e IKSVD method is proposed in the paper to solve the above problems. IKSVD uses self-adaptive matching pursuit (SAMP) [28] for sparse coding instead of OMP in KSVD. Sparsity of the sparse coefficients could be described adaptively by changing step length, and the reason is that sparsity of the sparse coefficients does not need to be known.…”

Section: Iksvdmentioning

confidence: 99%

“…e sparse representation self-learning dictionary method such as KSVD could capture the characteristic components hidden in the vibration signal without prior knowledge of the analyzed signal such as wavelet transform. Besides, it has virtue of strong noise robustness and has been applied in fault diagnosis of rotating machinery widely [26][27][28]. Consequently, a rotating machinery fault diagnosis method by combing sparse representation self-learning dictionary with FSC is proposed in the paper.…”

Section: Introductionmentioning

confidence: 99%

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Fast Spectral Correlation Based on Sparse Representation Self-Learning Dictionary and Its Application in Fault Diagnosis of Rotating Machinery

Wang

2020

Complexity

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Rolling element bearing and gear are the typical supporting or rotating parts in mechanical equipment, and it has important economy and security to realize their quick and accurate fault detection. As one kind of powerful cyclostationarity signal analyzing method, spectral correlation (SC) could identify the impulsive characteristic component buried in the vibration signals of rotating machinery effectively. However, the fault feature such as impulsive characteristic component is often interfered by other background noise, and the situation is serious especially in early weak fault stage. Besides, the traditional SC method has a drawback of low computation efficiency which hinders its wide application to some extent. To address the above problems, an impulsive feature-enhanced method which combines fast spectral correlation (FSC) with sparse representation self-learning dictionary is proposed in the paper. Firstly, the sparse representation self-learning dictionary method-K-means singular value decomposition (KSVD) is improved and the improved KSVD (IKSVD) method is used to denoise the original signal, and the periodic impulses are highlighted. Then, the FSC algorithm is applied on the denoised signal and spectral correlation image could be obtained. Finally, the calculated enhanced envelope spectrum (EES) of the denoised signal is obtained by using the spectral correlation image to identify the accurate fault position. The feasibility and superiority of the proposed method is verified through simulation, experiment, and engineering application.

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Section: Iksvdmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Fast Spectral Correlation Based on Sparse Representation Self-Learning Dictionary and Its Application in Fault Diagnosis of Rotating Machinery

Wang

2020

Complexity

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“…It reconstructs channel information through stage by stage estimation of the sparsity level and the true support set of the target signals [18]. However, one of the shortcomings of SAMP is the fixed step size, which easily results in the contradiction between the convergence speed and the recovery accuracy of the algorithm [19,20].…”

Section: Background Knowledgementioning

confidence: 99%

“…The channel of other receive antennas is also estimated according to the above methods. Unlike conventional greedy iteration CS reconstruction schemes such as OMP and CoSaMP, where the number of iterations is the value of channel sparsity level [20], the proposed scheme stops iteration only when the residual is close to 0 (‖r t ‖ 2 < ε). Therefore, the reconstruction accuracy can be guaranteed.…”

Section: Inputmentioning

confidence: 99%

Sparse massive MIMO-OFDM channel estimation based on compressed sensing over frequency offset environment

et al. 2019

EURASIP J. Adv. Signal Process.

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In massive MIMO-OFDM systems, channel estimation is a significant module which can be utilized to eliminate multipath interference. However, in realistic communication systems, carrier frequency offset (CFO), which often exists in receive end, will deteriorate the performance of channel estimation. One of the effective solutions is to compensate CFO via the help of pseudo-noise (PN) sequence. At the beginning of this paper, to reduce system complexity and correctly compensate CFO, we propose an improved OFDM frame structure. Subsequently, we theoretically analyze the catastrophic influence of CFO on conventional PN-sequence-based compressed sensing (CS) channel estimation scheme. As our solution, based on the improved OFDM frame structure, a novel massive MIMO-OFDM channel estimation method under CFO environment is proposed. It first estimates CFO by utilizing differential correlation algorithm. Thereby, the interference caused by CFO can be eliminated. Then, relying on the PN sequence, the partial common support (PCS) information of each channel is obtained. Finally, using the PCS information as a priori information, we improve the CS reconstruction scheme to estimate the accurate channel. The simulation result shows that the proposed scheme demonstrates better MSE and BER performance than other mentioned schemes. The major advantage of our scheme is its anti-CFO ability and independence to channel sparsity level. Therefore, the proposed scheme is meaningful for practical use.

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“…Thus, there is a trade‐off between step size and overall performance of the algorithm. This is addressed by making the step‐size variable or adaptive as proposed in the previous studies . This paper mainly contributes to the following: Design of STBC‐SM‐based hybrid MIMO‐ACO‐OFDM for indoor VLC and its channel estimation using CS algorithms. Proposing a new bandwidth‐efficient CS algorithm for channel estimation with faster convergence and adaptive step size to determine sparsity using deterministic threshold. …”

Section: Introductionmentioning

confidence: 99%

Compressed sensing channel estimation for STBC‐SM based hybrid MIMO‐OFDM system for visible light communication

Manur

Ali

2020

Int J Communication

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Summary This paper presents the idea of sparse channel estimation using compressed sensing (CS) method for space–time block coding (STBC), and spatially multiplexing (SM) derived hybrid multiple‐input multiple‐output (MIMO) Asymmetrically clipped optical‐orthogonal frequency division multiplexing (ACO‐OFDM) optical wireless communication system. This hybrid system accounts multiplexing gain of SM and diversity gain of STBC technique. We present a new variant of sparsity adaptive matching pursuit (SaMP) algorithm called dynamic step‐size SaMP (DSS‐SaMP) algorithm. It makes use of the inherent and implicit structure of SaMP, along with dynamic adaptivity of step‐size feature which is compatible with the energy of the input signal, thus the name dynamic step size. Existing CS‐based recovery algorithms like orthogonal matching pursuit, SaMP, adaptive step‐size SaMP, and proposed DSS‐SaMP were compared for hybrid MIMO‐ACO‐OFDM visible light communication system. The performance analysis is demonstrated through simulation results with respect to bit error rate, symbol error rate, mean square error, computational complexity, and peak‐to‐average power ratio. Simulation results show that the proposed technique gives improved performance and lesser computational complexity in comparison with conventional estimation algorithms.

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Variable step size stagewise adaptive matching pursuit algorithm for image compressed sensing

Cited by 13 publications

References 20 publications

Fast Spectral Correlation Based on Sparse Representation Self-Learning Dictionary and Its Application in Fault Diagnosis of Rotating Machinery

Fast Spectral Correlation Based on Sparse Representation Self-Learning Dictionary and Its Application in Fault Diagnosis of Rotating Machinery

Sparse massive MIMO-OFDM channel estimation based on compressed sensing over frequency offset environment

Compressed sensing channel estimation for STBC‐SM based hybrid MIMO‐OFDM system for visible light communication

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